When I was a teenager, I had one terrible habit that drove my mother over-the-edge crazy. (OK: I had more than one. But hey, “driving your mother crazy” is part of the official job description for “teen-age girl.” I looked it up.)
My particular expertise was in the fine craft of strategically omitting information that would’ve assuredly had a negative impact on my desired outcome.
For example, I would ask if I could go to my best friend Tracy’s house for the night, but I would leave out the fact that we would be stopping by bad-boy Tom’s house for a “my parents are away” party. This fact would of course have resulted in my having to stay home – that is, in my view, in the worst outcome imaginable. (Yes, I did consider law school early in life.)
In my defense, there were times when I didn’t know bad-boy Tom was having a party until after I’d received permission to go to Tracy’s house. On these occasions I asked for my Mom’s consent based on the best available incomplete information. Of course, as is the way with all mothers, she eventually found out where I’d been (even on the occasions when no police were involved). As a result, each of my subsequent requests for permission to go out elicited an ever more rigorous line of inquiry from her.
My (now) fond memory of these mother-daughter tussles was prompted by a recent article I read in the New York Times: “The Experts Were Wrong About the Best Places for Better and Cheaper Health Care.” Let me tell you why.
Until recently, the largest and best data-set available for the analysis and study of healthcare delivery in the U.S. was that based on Medicare claim data. Private-insurance statistics have long been almost entirely inaccessible for the same type of analysis and scrutiny, as they are held and managed by private companies that are not required to make them public.
This situation has left us scant choice but to make assumptions and decisions about how our healthcare system does and should deliver care using what I have come to term “best available incomplete information (BAII).”
As highlighted in the article I’ve cited above, Medicare data have revealed enormous amounts of information about regional differences in Medicare spending, which are driven mostly by the amount of healthcare patients receive, not the price per service.
Even more important, Medicare data reveal that places delivering lots of medical services to patients often do not have any better health outcomes than those locations delivering less medical care at lower cost.
These findings based on Medicare data have, by and large, been reduced to one simple message: if all healthcare systems could deliver care in the same way these low-cost ones do, the country’s notoriously high medical costs could be controlled, and might even decline.
On the face of it, this makes perfect sense. What’s missing, however, is how these systems are performing on the delivery of care to their non-Medicare patients. Are the results observed in one cohort of patients (Medicare) also the results for all other non-Medicare cohorts (private insurance, self-pay, etc.)? Data newly available from the Health Care Cost Institute (HCCI) about a large number of private insurance plans offer new hope that we may begin to answer these and other important questions more fully.
As a first high-level analysis described by the Times article reveals, places in the U.S. that have been heralded for low-cost, high-quality care delivered to Medicare patients are not necessarily performing in the same way for their private-insurance patients.
You can see these findings displayed in the side-by-side choropleth maps below.
Displaying the data like this reveals that (for example) although Alaska’s per capita Medicare spending is average as compared to all other areas in the U.S., its per capita private-insurance spending is above average. The data reveal a similar pattern for several other areas in the states of Idaho, Michigan, and New Hampshire (for example), where Medicare costs are either average or below average, but private-insurance spending is above average.
This isn’t the only observable difference. Interestingly, in places like my home state of Massachusetts, the opposite of the above is true: Medicare spending is above average, while spending on private insurance is average across the state.
The Times article displays this information on the maps above and also in this simple but effective graphic (click here to check a place near you).
I find these new data wildly interesting, am certain they will result in new findings, and devoutly hope that they will also lead to greater transparency in and other improvements to our healthcare system.
But this new information also serves as a serious and important reminder that we are all making decisions using the best available incomplete information currently available to us, and only that. As a result, we have to try to get better at understanding what it can and cannot enlighten us about, and how we will act when new information becomes available to us.
After I read the Times article and thought about its title, I found myself annoyed at what seemed a rather negative headline: “The Experts Were Wrong…” In fact, the experts were right about what the BAII they had at the time revealed. Was it the full story? Absolutely not. Do we know that full story yet? We do not: even this new analysis is missing data on patients insured by Blue Cross & Blue Shield and Medicaid, as well as on the under- and the un-insured. To put it another way: “We still don’t know what we don’t know.”
It seems to me that the only sensible path to improving our healthcare system is to commit ourselves to continually seeking new data, information, and knowledge to support better-informed decisions, and to seek the courage to adjust our sails and lead change by following – even when that path may be disappointing, confusing, or difficult – where the data lead.